1. Key Laboratory of Particle and Radiation Imaging of Ministry of Education, Department of Engineering Physics, Tsinghua University, Beijing 100084, China; 2. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China; 3. Department of Orthopedics, Aero Space Center Hospital, Beijing 100048, China
Abstract：Magnetic resonance temperature imaging is an important technique to ensure safe and effective use of tumor hyperthermia ablation by providing real-time, global temperature field monitoring. In clinical trials, however, the signal-to-noise ratio (SNR) of magnetic resonance temperature imaging is relatively low, with the signal quality degrading more with fast imaging sequences. To solve this problem, a bio-heat transfer based Kalman filtering model is developed for magnetic resonance temperature imaging where the bio-heat transfer equation is transformed into the form of a Kalman state transition matrix. Then the simulated temperature is combined with the measured temperature to create an accurate, high SNR estimated temperature. Clinical simulations show that this method reduces the temperature measurement root mean square error from 6℃ to 2℃ and the physical phantom experiment shows that this method reduces the root mean square error of the measured temperature and the true temperature from 1.927℃ to 0.735℃ while significantly improving the SNR.
图８ S B S方法和 BHT Ｇ K a l ma n方法重建结果与
金标准图之间在不同时间帧的 RMS E值
图９ (网络版彩图)仿体实验重建结果对比
图１ ０ (网络版彩图)仿体实验温度曲线
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2020, Vol. 60 
